(mobile_robot_pathplanning)
This project focuses on implementing a real-time path planning algorithm for the Firebird V robot in a two dimensional grid-based environment, with the goal of achieving autonomous following while avoiding obstacles. To overcome the limitations of computation and storage, we will use PC to facilitate efficient computation. The robot’s movement will be limited to turning 90 and 180 degrees and moving forward, backward, left, or right to simplify path planning. Additionally, we will explore the use of a gripper or robotic manipulator to enable the Firebird V to access goal objects that are near. Our objective is to develop a safe and reliable method of object retrieval that can be integrated into the robot’s autonomous following capabilities. The project presents several challenges, including optimizing the algorithm to function efficiently on the ATMEGA2560 microcontroller, balancing competing constraints such as computation, storage, and movement limitations. However, through this project, we hope to develop our skills in software and hardware engineering and gain comprehensive knowledge of implementing academic algorithms in real-world products. The results of this project could have practical applications in various industries, including manufacturing, agriculture, and logistics. Overall, this project is an exciting opportunity to apply academic concepts to a real-world problem and gain valuable experience in engineering.
autonomous navigation, real-time, firebird v, path planning, a* algorithm, gripper, image processing, xbee.
python manage.py runserver
python manage.py migrate
python manage.py createsuperuser
Get-ExecutionPolicy -List
Set-ExecutionPolicy Unrestricted
In conclusion, our project encountered a few challenges during the development phase. One of the major issues was drifting, despite using a stepper motor. This made it necessary to reduce the grid size to avoid errors. In the future, we may need to add localization methods to support larger environments. The lack of wireless communication was another issue, as it made it difficult to display the robot’s real-time path. Although we were able to view the output using the console while the robot was connected to the computer, there was no visual guide once it was unplugged. However, our prototype was able to meet all the requirements we had set at the beginning of the project. It can generate an optimal path from an initial starting point and re-generate an optimal path when obstacles are detected. Overall, it meets the basic requirements and can be further improved with additional features such as wireless compatibility.
- Aswin BKK license
- Copyright 2022 © Aswin BKK.